Field of the Invention
The present invention relates to an image processing apparatus, image processing method, and storage medium for performing a quantization process to form an image on a print medium.
Description of the Related Art
In the case of using a pseudo gradation method to print an image, it is necessary to quantize multi-valued image data, and as a quantization method used for the quantization, an error diffusion method and a dither method are known. In particular, the dither method that compares a preliminarily stored threshold value and a gradation value of multi-valued data with each other to determine dot printing or non-printing has a small processing load as compared with the error diffusion method, and is therefore widely used in many image processing apparatuses. In the case of such a dither method, in particular, dot dispersibility in a low gradation range becomes problematic; however, as a threshold value matrix for obtaining preferable dot dispersibility, a threshold value matrix having blue noise characteristics is proposed.
FIGS. 5A and 5B are diagrams illustrating blue noise characteristics and the human visual transfer function (VTF) at a visibility distance of 250 mm. In both drawings, the horizontal axis represents frequency (cycles/mm), with the frequency going lower proceeding to the left side of the graph, and the frequency going higher proceeding to the right side. Meanwhile, the vertical axis represents the power corresponding to each frequency.
Referring to FIG. 5A, blue noise characteristics feature a depressed low-frequency component, a sudden peak, and a flat high-frequency component. On the other hand, the human VTF illustrated in FIG. 5B is highly sensitive in the low-frequency region, and less sensitive in the high-frequency region. In other words, a low-frequency component is easy to notice, whereas a high-frequency component is difficult to notice. In light of such a visual transfer function, blue noise characteristics are made to have almost no power in the low-frequency region where the sensitivity of the visual transfer function is high (easy to notice), and have power in the high-frequency region where the sensitivity of the visual transfer function is low (difficult to notice). For this reason, when a person views an image obtained by performing a quantization process using a threshold value matrix having blue noise characteristics, dot imbalances and periodicities are hardly noticed, and the image is perceived as pleasant.
Blue noise characteristics as described above are defined and explained in much of the literature, such as Robert Ulichney, “Digital Halftoning”, The MIT Press Cambridge, Mass. (London, England). In addition, regarding the method of creating a threshold value matrix while controlling the frequency components, including the blue noise characteristics, the void-and-cluster method may be adopted. A method of creating a threshold value matrix using the void-and-cluster method is disclosed in Robert Ulichney, “The void-and-cluster method for dither array generation”, Proceedings SPIE, Human Vision, Visual Processing, Digital Displays IV, vol. 1913, pp. 332-343, 1993.
Meanwhile, among recent inkjet printing apparatuses, higher gradations and higher resolutions are being pursued, and apparatuses capable of printing dots at a high resolution such as 1200 dots per inch (dpi) or 2400 dpi are being provided. However, in the case of attempting to perform all of the signal value conversion for generating data printable by the printing apparatus from image data in a designated format at a high resolution of 1200 dpi or 2400 dpi, the large processing load and drop in throughput are causes for concern. For this reason, in many inkjet printing apparatuses, a method is adopted in which the primary image processing is performed at a comparatively low resolution of approximately 600 dpi, and after subsequently performing multi-valued quantization of respective image data to multiple levels, a binarization process matched to the printing resolution is additionally performed.
FIGS. 4A and 4B are diagrams illustrating specific examples of multi-valued quantization using a threshold value matrix. FIG. 4A illustrates the case of quantizing multi-valued input data In from 0 to 255 into three values from Level 0 to Level 2 using a threshold value matrix 400. In the threshold value matrix 400, threshold values from 0 to 127 are mapped to individual pixel areas with high dispersibility. The drawing illustrates the area of the threshold value matrix 400 as a 4-pixel by 4-pixel area for the sake of simplicity, but in actual practice, the threshold value matrix includes areas in which at least one or more of each of the threshold values from 0 to 127 are mapped.
When performing multi-valued quantization of multi-valued input data In with 256 values into 3 values, the region (0 to 255) of the multi-valued input data is divided into a first region (0 to 128) and a second region (129 to 255), and a binarization process is performed using a designated threshold value matrix for each of the regions. Subsequently, for the first region, the multi-valued input data In is quantized to Level 1 when greater than the corresponding threshold value, and quantized to Level 0 when less than or equal to the threshold value. For the second region, the multi-valued input data In is quantized to Level 2 when greater than the corresponding threshold value, and quantized to Level 1 when less than or equal to the threshold value. In this way, in a typical multi-valued quantization process, the region of the multi-valued input data In is divided into the number L of quantized levels minus 1 (L−1), and a binarization process is performed on each of the regions to obtain quantized values with L levels.
FIG. 4A specifically illustrates the quantization results for several pieces of multi-valued input data In. When In=0, the quantized value of all pixels becomes 0. When In=128, the quantized value of all pixels becomes 1. When In=255, the quantized value of all pixels becomes 2. Also, when 0<In<128, the quantized values are a mixture of the two levels 0 and 1. When 128<In<255, the quantized values are a mixture of the two levels 1 and 2.
FIG. 4B illustrates the degree of perceived graininess in the case of actually printing a dot pattern on a print medium according to the multi-valued input data In. As the graininess becomes higher, roughness in the image becomes more noticeable, leading to an unfavorable visual impression. When the multi-valued input data In is 0, or 128, or 255, the pixel areas are uniform, having the same quantized values. As a result, the same dot pattern is uniformly arranged over the entire image area, and graininess is kept to a minimum. Conversely, when the multi-valued input data In is 0<In<128, or 128<In<255, the two types of quantized values are mixed in the pixel areas. As a result, different dot patterns are interspersed over the entire image area, and graininess is increased. In particular, the graininess becomes the highest when In=64, at which there is an even mixture of the quantized values 0 and 1, and when In=192, at which there is an even mixture of the quantized values 1 and 2.
In such a situation, if the multi-valued input data In gradually changes from 0 to 255, the graininess changes most suddenly near In=128. Additionally, when a gradation pattern is printed such a change of graininess is perceived as a pseudo contour. In other words, with the typical multi-valued quantization process using the dither method of the related art, a continuity of density is obtained, but a continuity of graininess is not obtained.
Such discontinuity of graininess is caused by the existence of gradations for which all pixel areas of the threshold value matrix are uniformly set to a single quantized value (for example, In=128 results in a uniform quantized value of 1). For gradations uniformly set to a single quantized value in this way, identical dot patterns are repeatedly arranged, thereby causing the graininess to become extremely low compared to the neighboring gradations.
In light of such a phenomenon, Japanese Patent No. 4059701 discloses a multi-valued quantization method configured to not produce gradations uniformly set to a single quantized value. Specifically, for specific gradations near In=128, the multi-valued quantization method generates pixels whose quantized value becomes 2 even if In<128 and pixels whose quantized value becomes 0 even if In>128, resulting in a mixture of quantized values at the three levels 0, 1, and 2 within the threshold value matrix area. Additionally, the graininess at In=128 is kept from becoming extremely low to moderate the discontinuity of graininess.
However, adopting the method in Japanese Patent No. 4059701 by using a threshold value matrix having blue noise characteristics produces a situation in which the advantageous effects of blue noise characteristics cannot be sufficiently exhibited. Hereinafter, a specific description will be given.
In a threshold value matrix having blue noise characteristics, by mapping dots to pixels corresponding to continuous threshold values including the minimum threshold value (0), blue noise characteristics with high dispersibility may be obtained. Consequently, even if dots are mapped to pixels corresponding to continuous threshold values, sufficient blue noise characteristics cannot be obtained when pixels corresponding to the minimum value or a nearby threshold value (0) are not included.
FIGS. 6A to 6C are diagrams illustrating the frequency characteristics in the case of mapping dots to a specific threshold value region in a threshold value matrix having blue noise characteristics. Similar to FIGS. 5A and 5B, the horizontal axis represents spatial frequency (cycles/mm), while the vertical axis represents power. Herein, a threshold value matrix in which threshold values from 1 to 127 are arranged two at a highly dispersed state in a 16×16 pixel area is used. FIG. 6A illustrates the frequency characteristics in the case of mapping dots to the threshold values from 0 to 29, while FIG. 6B illustrates the frequency characteristics in the case of mapping dots to the threshold values from 30 to 97, and FIG. 6C illustrates the frequency characteristics in the case of mapping dots to the threshold values from 98 to 127.
In FIG. 6A, since dots are mapped to pixels from the minimum threshold value (0) to (29), the blue noise characteristics are realized comparatively accurately, with a depressed low-frequency component, a sudden peak, and a flat high-frequency component. However, in FIG. 6B, dots are not mapped to threshold values including the minimum value (0 to 29), and instead mapped only to threshold values in the middle (30 to 97), and thus the low-frequency component increases, the peak is softened, and sufficient blue noise characteristics are not obtained. FIG. 6C, in which dots are mapped only to the threshold values 98 to 127, is similar. Additionally, the dispersibility of an image obtained by mapping dots to threshold value region excluding the threshold value region that includes the minimum value (0), as in FIGS. 6B and 6C, becomes lower than the dispersibility of an image obtained by mapping dots to threshold value region including the minimum value, as in FIG. 6A.
In the case of Japanese Patent No. 4059701, in the region of low multi-valued input data In (In=0 to 124), as the value of the multi-valued input data rises by 1, the pixels having a quantized value of 1 increase by 1, and the pixels having a quantized value of 0 (paper white) decrease by 1. Additionally, in this region (In=0 to 124), blue noise characteristics are realized for any gradation. However, in the region (In=125 to 132) near the midpoint (In=128) of the multi-valued input data In, pixels having a quantized value of 0 are maintained even if the value of the multi-valued input data rises by 1, and the quantized value of pixels having a quantized value of 1 is replaced by a value of 2. For this reason, when no distinction is made between the quantized values of 1 and 2, the dot arrangement remains the same.
However, as for pixels having a quantized value of 0 (paper white) in this gradation region (In=125 to 132), frequency characteristics like in FIG. 6C are exhibited, and the region is perceived as being comparatively grainy. In other words, in the case of adopting Japanese Patent No. 4059701, the discontinuity of graininess as illustrated in FIG. 4B is moderated, but the region of comparatively grainy gradations is extended.